NaiveAI Releases and Open-Sources the Naive-N0.5-Flash Model
BIBIBI
AT A GLANCE
NaiveAI released and open-sourced Naive-N0.5-Flash, and AI participated in research and development optimization.
Article
Dai Jifeng’s NaiveAI open-sources its first model:AI participated in 6 days of 151 rounds of optimization
Beating AI flash news, NaiveAI, founded by Tsinghua University associate professor Dai Jifeng, released Naive-N0.5-Flash and made the model weights available. The model was adapted from Xiaomi MiMo-V2.5 Base and open-sourced under the MIT protocol.
NaiveAI retained the original MoE architecture, with a focus on changing the attention mechanism. The team replaced global attention with sliding-window attention and DeepSeek sparse attention, then continued training 3250000000000 tokens. The adapted model natively supports a 1000000-token context, reducing the computational cost of processing extremely long text.AI also directly participated in the research and development. It wrote code, ran experiments, analyzed results, and continued optimization, while researchers set the direction and made key decisions. Using the inference system NaiveRT as an example, the team conducted 6 days of 151 rounds of optimization experiments, of which 63 rounds were adopted.
The officially reported peak inference speed was 2122 tok/s, but the test conditions were unusual: using 8 GPU, with thinking mode disabled, excluding input-processing time, and taking the best result from 41 requests at 1 seconds. In standard mode, the official figure was approximately 50 tok/s per user.
NaiveAI released Naive-N0.5-Flash and made the model weights available.
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The model was adapted from Xiaomi MiMo-V2.5 Base and open-sourced under the MIT protocol.
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The model retains the MoE architecture and uses sliding-window attention and DeepSeek sparse attention.
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The team continued training 3250000000000 tokens, and the model natively supports a 1000000-token context.
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NaiveRT conducted 6 days of 151 optimization experiments, of which 63 rounds were adopted.
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The officially reported peak inference speed was 2122 tok/s, and approximately 50 tok/s。
AI-assisted interpretation
The following is analysis, separate from reported facts. Verify important claims independently.
NaiveAI open-sourced an existing model after making structural and training adjustments. During research and development,AI was responsible for writing code, running experiments, analyzing results, and continuing optimization, while researchers determined the direction and made key decisions.
Why it matters to readers
This indicates that AI was used in the model research and development process, and that the model claims to support extremely long contexts and relatively high inference speeds.
You can understand it as follows: the team had AI participate in developing the model, while also making the model weights public so that others can learn about and use them under the MIT protocol.
Risks and unknowns
The peak inference speed test used 8 GPUs,GPU with thinking mode disabled and input-processing time excluded, and took the best result from 41 requests at 1 seconds.
The materials did not provide independent testing of the 1000000-token context capability or inference speed.
The material does not specify the model’s deployment costs, hardware requirements, or subsequent commercialization plans.
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MoE
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